COMPARISON WITH PRIOR LITERATURE
Include needed packages
# install.packages("tidyverse")
library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.4.4## -- Attaching packages ---------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.0 v purrr 0.3.2
## v tibble 1.4.2 v dplyr 0.7.8
## v tidyr 0.8.3 v stringr 1.2.0
## v readr 1.3.1 v forcats 0.2.0## Warning: package 'ggplot2' was built under R version 3.4.4## Warning: package 'tibble' was built under R version 3.4.4## Warning: package 'tidyr' was built under R version 3.4.4## Warning: package 'readr' was built under R version 3.4.4## Warning: package 'purrr' was built under R version 3.4.4## Warning: package 'dplyr' was built under R version 3.4.4## Warning: package 'forcats' was built under R version 3.4.2## -- Conflicts ------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# install.packages("plotly")
library(plotly)## Warning: package 'plotly' was built under R version 3.4.4##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutLoad database
prior_literature <- read.csv("I:\\PROJECTS\\Cost of Status Epilepticus\\Priorliterature.csv", header=TRUE)
prior_literature[1:5, 1:5]## Data Median p25 p75 NRSEMedian
## 1 KID2016 8749 4875 19067 6625
## 2 KID2012 7690 3893 17247 5157
## 3 KID2009 6529 3370 14854 4374
## 4 NIS2016 14678 7203 28388 9127
## 5 NIS2012 13874 6699 29176 7574Inflation factor to 2019 dollars (from the Bureau of Labor Statistics)
i2016to2019 <- 1.06
i2012to2019 <- 1.11
i2011to2019 <- 1.14
i2010to2019 <- 1.16
i2009to2019 <- 1.19
i2008to2019 <- 1.19
i2007to2019 <- 1.24Create dataframe for KID with 2019 USA dollar estimations
KID <- data.frame(type <- c("All SE", "All SE", "All SE",
"NRSE", "NRSE", "NRSE",
"RSE", "RSE", "RSE",
"SRSE", "SRSE", "SRSE",
"All SE", "All SE", "All SE",
"NRSE", "NRSE", "NRSE",
"RSE", "RSE", "RSE",
"SRSE", "SRSE", "SRSE",
"All SE", "All SE", "All SE",
"NRSE", "NRSE", "NRSE",
"RSE", "RSE", "RSE",
"SRSE", "SRSE", "SRSE"),
cost2019 <- c(prior_literature$Median[1]*i2016to2019, prior_literature$p25[1]*i2016to2019, prior_literature$p75[1]*i2016to2019,
prior_literature$NRSEMedian[1]*i2016to2019, prior_literature$NRSEp25[1]*i2016to2019, prior_literature$NRSEp75[1]*i2016to2019,
prior_literature$RSEMedian[1]*i2016to2019, prior_literature$RSEp25[1]*i2016to2019, prior_literature$RSEp75[1]*i2016to2019,
prior_literature$SRSEMedian[1]*i2016to2019, prior_literature$SRSEp25[1]*i2016to2019, prior_literature$SRSEp75[1]*i2016to2019,
prior_literature$Median[2]*i2012to2019, prior_literature$p25[2]*i2012to2019, prior_literature$p75[2]*i2012to2019,
prior_literature$NRSEMedian[2]*i2012to2019, prior_literature$NRSEp25[2]*i2012to2019, prior_literature$NRSEp75[2]*i2012to2019,
prior_literature$RSEMedian[2]*i2012to2019, prior_literature$RSEp25[2]*i2012to2019, prior_literature$RSEp75[2]*i2012to2019,
prior_literature$SRSEMedian[2]*i2012to2019, prior_literature$SRSEp25[2]*i2012to2019, prior_literature$SRSEp75[2]*i2012to2019,
prior_literature$Median[3]*i2009to2019, prior_literature$p25[3]*i2009to2019, prior_literature$p75[3]*i2009to2019,
prior_literature$NRSEMedian[3]*i2009to2019, prior_literature$NRSEp25[3]*i2009to2019, prior_literature$NRSEp75[3]*i2009to2019,
prior_literature$RSEMedian[3]*i2009to2019, prior_literature$RSEp25[3]*i2009to2019, prior_literature$RSEp75[3]*i2009to2019,
prior_literature$SRSEMedian[3]*i2009to2019, prior_literature$SRSEp25[3]*i2009to2019, prior_literature$SRSEp75[3]*i2009to2019))
colnames(KID) <- c("type", "cost2019")Create plot for KID with 2019 USA dollar estimations
# All categories
KIDplot <- ggplot(aes(x=type, y=cost2019), data=KID) + geom_point(color=c("darkgreen", "darkgreen", "darkgreen",
"darkgreen", "darkgreen", "darkgreen",
"darkgreen", "darkgreen", "darkgreen",
"darkgreen", "darkgreen", "darkgreen",
"orange", "orange", "orange",
"orange", "orange", "orange",
"orange", "orange", "orange",
"orange", "orange", "orange",
"pink", "pink", "pink",
"pink", "pink", "pink",
"pink", "pink", "pink",
"pink", "pink", "pink"),
size=c(10, 5, 5,
10, 5, 5,
10, 5, 5,
10, 5, 5,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3),
shape=c(19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18),
alpha=c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4)) +
theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
axis.text.x = element_text(face = "bold", size = 10),
axis.text.y = element_text(face = "bold", size = 10),
axis.title.x = element_text(face = "bold", size = 14),
axis.title.y = element_text(face = "bold", size = 14),
panel.border = element_blank(),
legend.title = element_blank()) +
scale_x_discrete(limits=c("NRSE", "RSE", "SRSE", "All SE")) +
scale_y_continuous(limits = c(0, 250000)) +
xlab("Type of Status Epilepticus") + ylab("Cost (in 2019 USA dollars)")
KIDplot # Regular plot
ggplotly(KIDplot) # Interactive plot# Without SRSE
KIDplotwithoutSRSE <- ggplot(aes(x=type, y=cost2019), data=KID[KID$type=="All SE" | KID$type=="NRSE" | KID$type=="RSE", ]) + geom_point(color=c( "darkgreen", "darkgreen", "darkgreen",
"darkgreen", "darkgreen", "darkgreen",
"darkgreen", "darkgreen", "darkgreen",
"orange", "orange", "orange",
"orange", "orange", "orange",
"orange", "orange", "orange",
"pink", "pink", "pink",
"pink", "pink", "pink",
"pink", "pink", "pink"),
size=c(10, 5, 5,
10, 5, 5,
10, 5, 5,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3),
shape=c(19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18),
alpha=c(1, 1, 1, 1, 1, 1, 1, 1, 1,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4)) +
theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
axis.text.x = element_text(face = "bold", size = 10),
axis.text.y = element_text(face = "bold", size = 10),
axis.title.x = element_text(face = "bold", size = 14),
axis.title.y = element_text(face = "bold", size = 14),
panel.border = element_blank(),
legend.title = element_blank()) +
scale_x_discrete(limits=c("NRSE", "RSE", "All SE")) +
scale_y_continuous(limits = c(0, 25000)) +
xlab("Type of Status Epilepticus") + ylab("Cost (in 2019 USA dollars)")
KIDplotwithoutSRSE # Regular plot
ggplotly(KIDplotwithoutSRSE) # Interactive plotCreate dataframe for NIS with 2019 USA dollar estimations
NIS <- data.frame(type <- c("All SE", "All SE", "All SE",
"NRSE", "NRSE", "NRSE",
"RSE", "RSE", "RSE",
"SRSE", "SRSE", "SRSE",
"All SE", "All SE", "All SE",
"NRSE", "NRSE", "NRSE",
"RSE", "RSE", "RSE",
"SRSE", "SRSE", "SRSE",
"All SE", "All SE", "All SE",
"NRSE", "NRSE", "NRSE",
"RSE", "RSE", "RSE",
"SRSE", "SRSE", "SRSE",
"All SE", "All SE", "All SE",
"NRSE", "NRSE", "NRSE",
"RSE", "RSE", "RSE",
"SRSE", "SRSE", "SRSE",
"All SE", "All SE", "All SE",
"NRSE", "NRSE", "NRSE",
"RSE", "RSE", "RSE",
"SRSE", "SRSE", "SRSE",
"All SE", "All SE", "All SE",
"NRSE", "NRSE", "NRSE",
"RSE", "RSE", "RSE",
"SRSE", "SRSE", "SRSE",
"All SE", "All SE", "All SE",
"NRSE", "NRSE", "NRSE",
"RSE", "RSE", "RSE",
"SRSE", "SRSE", "SRSE"),
cost2019 <- c(prior_literature$Median[4]*i2016to2019, prior_literature$p25[4]*i2016to2019, prior_literature$p75[4]*i2016to2019,
prior_literature$NRSEMedian[4]*i2016to2019, prior_literature$NRSEp25[4]*i2016to2019, prior_literature$NRSEp75[4]*i2016to2019,
prior_literature$RSEMedian[4]*i2016to2019, prior_literature$RSEp25[4]*i2016to2019, prior_literature$RSEp75[4]*i2016to2019,
prior_literature$SRSEMedian[4]*i2016to2019, prior_literature$SRSEp25[4]*i2016to2019, prior_literature$SRSEp75[4]*i2016to2019,
prior_literature$Median[5]*i2012to2019, prior_literature$p25[5]*i2012to2019, prior_literature$p75[5]*i2012to2019,
prior_literature$NRSEMedian[5]*i2012to2019, prior_literature$NRSEp25[5]*i2012to2019, prior_literature$NRSEp75[5]*i2012to2019,
prior_literature$RSEMedian[5]*i2012to2019, prior_literature$RSEp25[5]*i2012to2019, prior_literature$RSEp75[5]*i2012to2019,
prior_literature$SRSEMedian[5]*i2012to2019, prior_literature$SRSEp25[5]*i2012to2019, prior_literature$SRSEp75[5]*i2012to2019,
prior_literature$Median[6]*i2011to2019, prior_literature$p25[6]*i2011to2019, prior_literature$p75[6]*i2011to2019,
prior_literature$NRSEMedian[6]*i2011to2019, prior_literature$NRSEp25[6]*i2011to2019, prior_literature$NRSEp75[6]*i2011to2019,
prior_literature$RSEMedian[6]*i2011to2019, prior_literature$RSEp25[6]*i2011to2019, prior_literature$RSEp75[6]*i2011to2019,
prior_literature$SRSEMedian[6]*i2011to2019, prior_literature$SRSEp25[6]*i2011to2019, prior_literature$SRSEp75[6]*i2011to2019,
prior_literature$Median[7]*i2010to2019, prior_literature$p25[7]*i2010to2019, prior_literature$p75[7]*i2010to2019,
prior_literature$NRSEMedian[7]*i2010to2019, prior_literature$NRSEp25[7]*i2010to2019, prior_literature$NRSEp75[7]*i2010to2019,
prior_literature$RSEMedian[7]*i2010to2019, prior_literature$RSEp25[7]*i2010to2019, prior_literature$RSEp75[7]*i2010to2019,
prior_literature$SRSEMedian[7]*i2010to2019, prior_literature$SRSEp25[7]*i2010to2019, prior_literature$SRSEp75[7]*i2010to2019,
prior_literature$Median[8]*i2009to2019, prior_literature$p25[8]*i2009to2019, prior_literature$p75[8]*i2009to2019,
prior_literature$NRSEMedian[8]*i2009to2019, prior_literature$NRSEp25[8]*i2009to2019, prior_literature$NRSEp75[8]*i2009to2019,
prior_literature$RSEMedian[8]*i2009to2019, prior_literature$RSEp25[8]*i2009to2019, prior_literature$RSEp75[8]*i2009to2019,
prior_literature$SRSEMedian[8]*i2009to2019, prior_literature$SRSEp25[8]*i2009to2019, prior_literature$SRSEp75[8]*i2009to2019,
prior_literature$Median[9]*i2008to2019, prior_literature$p25[9]*i2008to2019, prior_literature$p75[9]*i2008to2019,
prior_literature$NRSEMedian[9]*i2008to2019, prior_literature$NRSEp25[9]*i2008to2019, prior_literature$NRSEp75[9]*i2008to2019,
prior_literature$RSEMedian[9]*i2008to2019, prior_literature$RSEp25[9]*i2008to2019, prior_literature$RSEp75[9]*i2008to2019,
prior_literature$SRSEMedian[9]*i2008to2019, prior_literature$SRSEp25[9]*i2008to2019, prior_literature$SRSEp75[9]*i2008to2019,
prior_literature$Median[10]*i2007to2019, prior_literature$p25[10]*i2007to2019, prior_literature$p75[10]*i2007to2019,
prior_literature$NRSEMedian[10]*i2007to2019, prior_literature$NRSEp25[10]*i2007to2019, prior_literature$NRSEp75[10]*i2007to2019,
prior_literature$RSEMedian[10]*i2007to2019, prior_literature$RSEp25[10]*i2007to2019, prior_literature$RSEp75[10]*i2007to2019,
prior_literature$SRSEMedian[10]*i2007to2019, prior_literature$SRSEp25[10]*i2007to2019, prior_literature$SRSEp75[10]*i2007to2019))
colnames(NIS) <- c("type", "cost2019")Create plot for NIS with 2019 USA dollar estimations
# All categories
NISplot <- ggplot(aes(x=type, y=cost2019), data=NIS) + geom_point(color=c("darkgreen", "darkgreen", "darkgreen",
"darkgreen", "darkgreen", "darkgreen",
"darkgreen", "darkgreen", "darkgreen",
"darkgreen", "darkgreen", "darkgreen",
"orange", "orange", "orange",
"orange", "orange", "orange",
"orange", "orange", "orange",
"orange", "orange", "orange",
"darkturquoise", "darkturquoise", "darkturquoise",
"darkturquoise", "darkturquoise", "darkturquoise",
"darkturquoise", "darkturquoise", "darkturquoise",
"darkturquoise", "darkturquoise", "darkturquoise",
"blue", "blue", "blue",
"blue", "blue", "blue",
"blue", "blue", "blue",
"blue", "blue", "blue",
"pink", "pink", "pink",
"pink", "pink", "pink",
"pink", "pink", "pink",
"pink", "pink", "pink",
"firebrick", "firebrick", "firebrick",
"firebrick", "firebrick", "firebrick",
"firebrick", "firebrick", "firebrick",
"firebrick", "firebrick", "firebrick",
"darkgoldenrod", "darkgoldenrod", "darkgoldenrod",
"darkgoldenrod", "darkgoldenrod", "darkgoldenrod",
"darkgoldenrod", "darkgoldenrod", "darkgoldenrod",
"darkgoldenrod", "darkgoldenrod", "darkgoldenrod"),
size=c(10, 5, 5,
10, 5, 5,
10, 5, 5,
10, 5, 5,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3),
shape=c(19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18),
alpha=c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4)) +
theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
axis.text.x = element_text(face = "bold", size = 10),
axis.text.y = element_text(face = "bold", size = 10),
axis.title.x = element_text(face = "bold", size = 14),
axis.title.y = element_text(face = "bold", size = 14),
panel.border = element_blank(),
legend.title = element_blank()) +
scale_x_discrete(limits=c("NRSE", "RSE", "SRSE", "All SE")) +
scale_y_continuous(limits = c(0, 95000), breaks=c(0, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000)) +
xlab("Type of Status Epilepticus") + ylab("Cost (in 2019 USA dollars)")
NISplot # Regular plot
ggplotly(NISplot) # Interactive plot# Without SRSE
NISplotwithoutSRSE <- ggplot(aes(x=type, y=cost2019), data=NIS[NIS$type=="All SE" | NIS$type=="NRSE" | NIS$type=="RSE", ]) + geom_point(color=c( "darkgreen", "darkgreen", "darkgreen",
"darkgreen", "darkgreen", "darkgreen",
"darkgreen", "darkgreen", "darkgreen",
"orange", "orange", "orange",
"orange", "orange", "orange",
"orange", "orange", "orange",
"darkturquoise", "darkturquoise", "darkturquoise",
"darkturquoise", "darkturquoise", "darkturquoise",
"darkturquoise", "darkturquoise", "darkturquoise",
"blue", "blue", "blue",
"blue", "blue", "blue",
"blue", "blue", "blue",
"pink", "pink", "pink",
"pink", "pink", "pink",
"pink", "pink", "pink",
"firebrick", "firebrick", "firebrick",
"firebrick", "firebrick", "firebrick",
"firebrick", "firebrick", "firebrick",
"darkgoldenrod", "darkgoldenrod", "darkgoldenrod",
"darkgoldenrod", "darkgoldenrod", "darkgoldenrod",
"darkgoldenrod", "darkgoldenrod", "darkgoldenrod"),
size=c(10, 5, 5,
10, 5, 5,
10, 5, 5,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3,
6, 3, 3),
shape=c(19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18,
19, 18, 18),
alpha=c(1, 1, 1, 1, 1, 1, 1, 1, 1,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4)) +
theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
axis.text.x = element_text(face = "bold", size = 10),
axis.text.y = element_text(face = "bold", size = 10),
axis.title.x = element_text(face = "bold", size = 14),
axis.title.y = element_text(face = "bold", size = 14),
panel.border = element_blank(),
legend.title = element_blank()) +
scale_x_discrete(limits=c("NRSE", "RSE", "All SE")) +
scale_y_continuous(limits = c(0, 35000)) +
xlab("Type of Status Epilepticus") + ylab("Cost (in 2019 USA dollars)")
NISplotwithoutSRSE # Regular plot
ggplotly(NISplotwithoutSRSE) # Interactive plot